The Tibetan Plateau, the highest plateau in the world, has experienced strong climate warming during the last few decades. The greater increase of temperature at higher elevations may have strong impacts on the vertical movement of vegetation activities on the plateau. Although satellite-based observations have explored this issue, these observations were normally provided by the coarse satellite data with a spatial resolution of more than hundreds of meters (e.g., GIMMS and MODIS), which could lead to serious mixed-pixel effects in the analyses. In this study, we employed the medium-spatial-resolution Landsat NDVI data (30 m) during 1990–2019 and investigated the relationship between temperature and the elevation-dependent vegetation changes in six mountainous regions on the Tibetan Plateau. Particularly, we focused on the elevational movement of the vegetation greenness isoline to clarify whether the vegetation greenness isoline moves upward during the past three decades because of climate warming. Results show that vegetation greening occurred in all six mountainous regions during the last three decades. Increasing temperatures caused the upward movement of greenness isoline at the middle and high elevations (>4000 m) but led to the downward movement at lower elevations for the six mountainous regions except for Nyainqentanglha. Furthermore, the temperature sensitivity of greenness isoline movement changes from the positive value to negative value by decreasing elevations, suggesting that vegetation growth on the plateau is strongly regulated by other factors such as water availability. As a result, the greenness isoline showed upward movement with the increase of temperature for about 59% pixels. Moreover, the greenness isoline movement increased with the slope angles over the six mountainous regions, suggesting the influence of terrain effects on the vegetation activities. Our analyses improve understandings of the diverse response of elevation-dependent vegetation activities on the Tibetan Plateau.
Modeling vegetation greenness and its climate sensitivity with deep‐learning technology
